Optimization Based Tuning of Autopilot Gains for a Fixed Wing UAV

Unmanned Aerial Vehicles (UAVs) have gained tremendous importance, in both Military and Civil, during first decade of this century. In a UAV, onboard computer (autopilot) autonomously controls the flight and navigation of the aircraft. Based on the aircraft role and flight envelope, basic to complex and sophisticated controllers are used to stabilize the aircraft flight parameters. These controllers constitute the autopilot system for UAVs. The autopilot systems, most commonly, provide lateral and longitudinal control through Proportional% Integral% Derivative (PID) controllers or Phase% lead or Lag Compensators. Various techniques are commonly used to 'tune' gains of these controllers. Some techniques used are, in% flight step% by% step tuning, software% in% loop or hardware% in% loop tuning methods. Subsequently, numerous in% flight tests are required to actually 'fine% tune' these gains. However, an optimization% based tuning of these PID controllers or compensators, as presented in this paper, can greatly minimize the requirement of in% flight 'tuning' and substantially reduce the risks and cost involved in flight% testing. Keywords—Unmanned aerial vehicle (UAV), autopilot, autonomous controls, PID controler gains tuning, optimization.

[1]  Jin Wang,et al.  Trajectory tracking of UAV using robust inventory control techniques , 2005, Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05..

[2]  Timothy W. McLain,et al.  Vision-based Target Geo-location using a Fixed-wing Miniature Air Vehicle , 2006, J. Intell. Robotic Syst..

[3]  Michael J. Allen,et al.  Guidance and Control of an Autonomous Soaring UAV , 2013 .

[4]  YangQuan Chen,et al.  Autopilots for small unmanned aerial vehicles: A survey , 2010 .

[5]  Timothy W. McLain,et al.  Autonomous Vehicle Technologies for Small Fixed-Wing UAVs , 2003, J. Aerosp. Comput. Inf. Commun..

[6]  Henrik Grankvist Autopilot Design and Path Planning for a UAV , 2006 .

[7]  Mansoor Ahsan,et al.  An Algorithm for Autonomous Aerial Navigation using MATLAB® Mapping Tool Box , 2012 .

[8]  Mandyam V. Srinivasan,et al.  Landing Strategies in Honeybees and Applications to Uninhabited Airborne Vehicles , 2004, Int. J. Robotics Res..

[9]  József Bokor,et al.  Longitudinal Motion Control of a High-Speed Supercavitation Vehicle , 2007 .

[10]  Feng Liang RAPID DEVELOPMENT OF UAV AUTOPILOT USING MATLAB/SIMULINK , 2002 .

[11]  M. Parnichkun,et al.  Attitude and heading control of an autonomous flying robot , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[12]  L. Smrcek,et al.  Simulation of UAV Systems , 2005 .

[13]  Jon Bernhard Høstmark Modelling Simulation and Control of Fixed-wing UAV: CyberSwan , 2007 .

[14]  Gary J. Balas,et al.  Software-Enabled Receding Horizon Control for Autonomous Unmanned Aerial Vehicle Guidance , 2006 .

[15]  Paul F. M. J. Verschure,et al.  A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance , 2007, Int. J. Robotics Res..

[16]  Michael A. Johnson,et al.  PID CONTROL: NEW IDENTIFICATION AND DESIGN METHODS , 2008 .

[17]  Wageeh Boles,et al.  Fixed-Wing Attitude Estimation Using Computer Vision Based Horizon Detection , 2007 .